Introduction

In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.

Load required R packages

suppressPackageStartupMessages({
  library(tibble)
  library(rcrossref)
  library(dplyr)
  library(tidyr)
  library(ggplot2)
  library(lubridate)
  library(gh)
  library(purrr)
  library(jsonlite)
  library(DT)
  library(plotly)
  library(citecorp)
  library(readr)
})
## Keep track of the source of each column
source_track <- c()

## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
  dcap <- lubridate::today()
} else {
  dcap <- ""
}
## Read archived version of summary data frame, to use for filling in 
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
  dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)

## Similarly for citation analysis, to avoid having to pull down the 
## same information multiple times
citations_archive <- readr::read_delim(
  url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
  col_types = cols(.default = "c"), col_names = TRUE,
  delim = "\t")

Collect information about papers

Pull down papers and citation info from Crossref

We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.

## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                              limit = lim)$data
i <- 1
while (nrow(papers) == i * lim) {
  papers <- dplyr::bind_rows(
    papers, 
    rcrossref::cr_works(filter = c(issn = "2475-9066"), 
                        limit = lim, offset = i * lim)$data)
  i <- i + 1
}
papers <- papers %>%
  dplyr::filter(type == "journal-article") 

## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
                                       papers$url[noaltid])

## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
  cit %>% dplyr::rename(citation_count = count), 
  by = c("alternative.id" = "doi")
)

## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")

source_track <- c(source_track, 
                  structure(rep("crossref", ncol(papers)), 
                            names = colnames(papers)))

Pull down info from Whedon API

For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.

whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
  url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
  simplifyDataFrame = FALSE
)
while (length(a) > 0) {
  whedon <- c(whedon, a)
  p <- p + 1
  a <- jsonlite::fromJSON(
    url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
    simplifyDataFrame = FALSE
  )
}

whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
  data.frame(api_title = w$title, 
             api_state = w$state,
             editor = paste(w$metadata$paper$editor, collapse = ","),
             reviewers = paste(w$reviewers, collapse = ","),
             nbr_reviewers = length(w$reviewers),
             repo_url = w$repository_url,
             review_issue_id = w$review_issue_id,
             doi = w$doi,
             prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
                                         w$meta_review_issue_id, NA_integer_),
             languages = paste(w$metadata$paper$languages, collapse = ","),
             archive_doi = w$metadata$paper$archive_doi)
}))

papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))

source_track <- c(source_track, 
                  structure(rep("whedon", length(setdiff(colnames(papers),
                                                         names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Combine with info from GitHub issues

From each pre-review and review issue, we extract information about review times and assigned labels.

## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues", 
             .limit = 5000, state = "all")
## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
  data.frame(title = i$title, 
             number = i$number,
             state = i$state,
             opened = i$created_at,
             closed = ifelse(!is.null(i$closed_at),
                             i$closed_at, NA_character_),
             ncomments = i$comments,
             labels = paste(setdiff(
               vapply(i$labels, getElement, 
                      name = "name", character(1L)),
               c("review", "pre-review", "query-scope", "paused")),
               collapse = ","))
}))

## Split into REVIEW, PRE-REVIEW, and other issues (the latter category 
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) & 
                                    !grepl("\\[REVIEW\\]", title))
dim(issother)
## [1] 108   7
head(issother)
##                                                                                                                                                                        title
## 1                                Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
## 2                                                                    State of the field: Do the authors describe how this software compares to other commonly-used packages?
## 3                         Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
## 4                                          A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
## 5                                          A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
## 6 Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support
##   number  state               opened               closed ncomments labels
## 1   3995 closed 2021-12-14T20:28:26Z 2021-12-14T20:28:29Z         2       
## 2   3989 closed 2021-12-10T18:07:16Z 2021-12-10T18:07:21Z         2       
## 3   3966 closed 2021-12-01T11:29:56Z 2021-12-01T11:30:01Z         1       
## 4   3952 closed 2021-11-24T15:25:26Z 2021-11-24T15:25:30Z         1       
## 5   3939 closed 2021-11-21T19:17:42Z 2021-11-21T19:17:47Z         1       
## 6   3923 closed 2021-11-18T19:57:53Z 2021-11-18T19:57:56Z         3
## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
  paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
  dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
  dplyr::mutate(alternative.id = paste0("10.21105/joss.", 
                                        nbrzeros,
                                        number)) %>%
  dplyr::select(-nbrzeros) %>% 
  dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
  dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))
## For pre-review and review issues, respectively, get the number of 
## issues closed each month, and the number of those that have the 
## 'rejected' label
review_rejected <- iss %>% 
  dplyr::filter(grepl("\\[REVIEW\\]", title)) %>% 
  dplyr::filter(!is.na(closed)) %>%
  dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
  dplyr::group_by(closedmonth) %>%
  dplyr::summarize(nbr_issues_closed = length(labels),
                   nbr_rejections = sum(grepl("rejected", labels))) %>%
  dplyr::mutate(itype = "review")

prereview_rejected <- iss %>% 
  dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>% 
  dplyr::filter(!is.na(closed)) %>%
  dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
  dplyr::group_by(closedmonth) %>%
  dplyr::summarize(nbr_issues_closed = length(labels),
                   nbr_rejections = sum(grepl("rejected", labels))) %>%
  dplyr::mutate(itype = "pre-review")

all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)
## For PRE-REVIEW issues, add information about the corresponding REVIEW 
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
  dplyr::filter(!grepl("withdrawn", labels)) %>%
  dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>% 
  dplyr::filter(!duplicated(title)) %>% 
  dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
  dplyr::rename_all(~ paste0("prerev_", .))

papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>% 
  dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
  dplyr::mutate(prerev_opened = as.Date(prerev_opened),
                prerev_closed = as.Date(prerev_closed),
                review_opened = as.Date(review_opened),
                review_closed = as.Date(review_closed)) %>% 
  dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
                days_in_rev = review_closed - review_opened,
                to_review = !is.na(review_opened))

source_track <- c(source_track, 
                  structure(rep("joss-github", length(setdiff(colnames(papers),
                                                              names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Add information from software repositories

## Reorder so that software repositories that were interrogated longest 
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
                  na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)
## [1] 1487
sum(is_github)
## [1] 1414
software_urls[!is_github]
##  [1] "https://gitlab.com/remram44/taguette"                                  
##  [2] "https://bitbucket.org/mituq/muq2.git"                                  
##  [3] "https://gitlab.inria.fr/bramas/tbfmm"                                  
##  [4] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
##  [5] "https://bitbucket.org/meg/cbcbeat"                                     
##  [6] "https://ts-gitlab.iup.uni-heidelberg.de/dorie/dorie"                   
##  [7] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/utopia"                 
##  [8] "https://gitlab.com/fduchate/predihood"                                 
##  [9] "https://gitlab.com/myqueue/myqueue"                                    
## [10] "https://bitbucket.org/hammurabicode/hamx"                              
## [11] "https://gitlab.inria.fr/miet/miet"                                     
## [12] "https://gitlab.com/cerfacs/batman"                                     
## [13] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/dantro"                 
## [14] "https://bitbucket.org/cardosan/brightway2-temporalis"                  
## [15] "https://savannah.nongnu.org/projects/complot/"                         
## [16] "https://gitlab.com/gdetor/genetic_alg"                                 
## [17] "https://gitlab.com/ffaucher/hawen"                                     
## [18] "http://mutabit.com/repos.fossil/grafoscopio/"                          
## [19] "https://gitlab.com/libreumg/dataquier.git"                             
## [20] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"    
## [21] "https://bitbucket.org/manuela_s/hcp/"                                  
## [22] "https://gitlab.com/jason-rumengan/pyarma"                              
## [23] "https://gitlab.com/emd-dev/emd"                                        
## [24] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"                
## [25] "https://gitlab.com/dlr-dw/ontocode"                                    
## [26] "https://bitbucket.org/rram/dvrlib/src/joss/"                           
## [27] "https://gitlab.com/sails-dev/sails"                                    
## [28] "https://gitlab.com/marinvaders/marinvaders"                            
## [29] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"                          
## [30] "https://bitbucket.org/clhaley/Multitaper.jl"                           
## [31] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"                   
## [32] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"          
## [33] "https://git.rwth-aachen.de/ants/sensorlab/imea"                        
## [34] "https://gitlab.com/project-dare/dare-platform"                         
## [35] "https://gitlab.com/vibes-developers/vibes"                             
## [36] "https://bitbucket.org/glotzer/rowan"                                   
## [37] "https://bitbucket.org/sciencecapsule/sciencecapsule"                   
## [38] "https://bitbucket.org/mpi4py/mpi4py-fft"                               
## [39] "https://gitlab.com/QComms/cqptoolkit"                                  
## [40] "https://bitbucket.org/cdegroot/wediff"                                 
## [41] "https://gitlab.com/toposens/public/ros-packages"                       
## [42] "https://gitlab.com/eidheim/Simple-Web-Server"                          
## [43] "https://framagit.org/GustaveCoste/eldam"                               
## [44] "https://bitbucket.org/basicsums/basicsums"                             
## [45] "https://gitlab.inria.fr/azais/treex"                                   
## [46] "https://www.idpoisson.fr/fullswof/"                                    
## [47] "https://gitlab.inria.fr/mosaic/bvpy"                                   
## [48] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"            
## [49] "https://gitlab.com/davidtourigny/dynamic-fba"                          
## [50] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"                   
## [51] "https://bitbucket.org/ocellarisproject/ocellaris"                      
## [52] "https://gitlab.com/dlr-ve/autumn/"                                     
## [53] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"                            
## [54] "https://bitbucket.org/berkeleylab/esdr-pygdh/"                         
## [55] "https://gitlab.com/materials-modeling/wulffpack"                       
## [56] "https://gitlab.com/moorepants/skijumpdesign"                           
## [57] "https://bitbucket.org/likask/mofem-cephas"                             
## [58] "https://bitbucket.org/dolfin-adjoint/pyadjoint"                        
## [59] "https://git.iws.uni-stuttgart.de/tools/frackit"                        
## [60] "https://bitbucket.org/cmutel/brightway2"                               
## [61] "https://gitlab.com/cosmograil/PyCS3"                                   
## [62] "https://gitlab.com/gims-developers/gims"                               
## [63] "https://bitbucket.org/dghoshal/frieda"                                 
## [64] "https://bitbucket.org/cloopsy/android/"                                
## [65] "https://gitlab.com/tesch1/cppduals"                                    
## [66] "https://gitlab.com/geekysquirrel/bigx"                                 
## [67] "https://doi.org/10.17605/OSF.IO/3DS6A"                                 
## [68] "https://gitlab.com/celliern/scikit-fdiff/"                             
## [69] "https://c4science.ch/source/tamaas/"                                   
## [70] "https://gitlab.com/datafold-dev/datafold/"                             
## [71] "https://gitlab.com/costrouc/pysrim"                                    
## [72] "https://gitlab.com/ampere2/metalwalls"                                 
## [73] "https://gitlab.com/energyincities/besos/"
df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
  u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
  if (grepl("/tree/", u0)) {
    u0 <- strsplit(u0, "/tree/")[[1]][1]
  }
  if (grepl("/blob/", u0)) {
    u0 <- strsplit(u0, "/blob/")[[1]][1]
  }
  info <- try({
    gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
  })
  languages <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"), 
       .limit = 500)
  })
  topics <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"), 
       .accept = "application/vnd.github.mercy-preview+json", .limit = 500)
  })
  contribs <- try({
    gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"), 
       .limit = 500)
  })
  if (!is(info, "try-error") && length(info) > 1) {
    if (!is(contribs, "try-error")) {
      if (length(contribs) == 0) {
        repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
      } else {
        repo_nbr_contribs <- length(contribs)
        repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
        if (is.na(repo_nbr_contribs_2ormore)) {
          print(contribs)
        }
      }
    } else {
      repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
    }
    
    if (!is(languages, "try-error")) {
      if (length(languages) == 0) {
        repolang <- ""
      } else {
        repolang <- paste(paste(names(unlist(languages)), 
                                unlist(languages), sep = ":"), collapse = ",")
      }
    } else {
      repolang <- ""
    }
    
    if (!is(topics, "try-error")) {
      if (length(topics$names) == 0) {
        repotopics <- ""
      } else {
        repotopics <- paste(unlist(topics$names), collapse = ",")
      }
    } else {
      repotopics <- ""
    }
    
    data.frame(repo_url = u, 
               repo_created = info$created_at,
               repo_updated = info$updated_at,
               repo_pushed = info$pushed_at,
               repo_nbr_stars = info$stargazers_count,
               repo_language = ifelse(!is.null(info$language),
                                      info$language, NA_character_),
               repo_languages_bytes = repolang,
               repo_topics = repotopics,
               repo_license = ifelse(!is.null(info$license),
                                     info$license$key, NA_character_),
               repo_nbr_contribs = repo_nbr_contribs,
               repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
    )
  } else {
    NULL
  }
})) %>%
  dplyr::mutate(repo_created = as.Date(repo_created),
                repo_updated = as.Date(repo_updated),
                repo_pushed = as.Date(repo_pushed)) %>%
  dplyr::distinct() %>%
  dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)

## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>% 
  dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
  dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)

papers <- papers %>% dplyr::left_join(df, by = "repo_url")

source_track <- c(source_track, 
                  structure(rep("sw-github", length(setdiff(colnames(papers),
                                                            names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Clean up a bit

## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
  dplyr::mutate(published.date = as.Date(published.print)) %>% 
  dplyr::mutate(
    halfyear = paste0(year(published.date), 
                      ifelse(month(published.date) <= 6, "H1", "H2"))
  ) %>% dplyr::mutate(
    halfyear = factor(halfyear, 
                      levels = paste0(rep(sort(unique(year(published.date))), 
                                          each = 2), c("H1", "H2")))
  ) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
papers <- papers %>% dplyr::distinct()

source_track <- c(source_track, 
                  structure(rep("cleanup", length(setdiff(colnames(papers),
                                                          names(source_track)))), 
                            names = setdiff(colnames(papers), names(source_track))))

Tabulate number of missing values

In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.

DT::datatable(
  data.frame(variable = colnames(papers),
             nbr_missing = colSums(is.na(papers))) %>%
    dplyr::mutate(source = source_track[variable]),
  escape = FALSE, rownames = FALSE, 
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

Number of published papers per month and year

ggplot(papers %>% 
         dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
         dplyr::group_by(pubmonth) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubmonth), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per month", caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

ggplot(papers %>% 
         dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
         dplyr::group_by(pubyear) %>%
         dplyr::summarize(npub = n()), 
       aes(x = factor(pubyear), y = npub)) + 
  geom_bar(stat = "identity") + theme_minimal() + 
  labs(x = "", y = "Number of published papers per year", caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.

ggplot(all_rejected, 
       aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) + 
  geom_bar(stat = "identity") + 
  theme_minimal() + 
  facet_wrap(~ itype, ncol = 1) + 
  labs(x = "Month of issue closing", y = "Fraction of issues rejected",
       caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

Citation distribution

Papers with 20 or more citations are grouped in the “>=20” category.

ggplot(papers %>% 
         dplyr::mutate(citation_count = replace(citation_count,
                                                citation_count >= 20, ">=20")) %>%
         dplyr::mutate(citation_count = factor(citation_count, 
                                               levels = c(0:20, ">=20"))) %>%
         dplyr::group_by(citation_count) %>%
         dplyr::tally(),
       aes(x = citation_count, y = n)) + 
  geom_bar(stat = "identity") + 
  theme_minimal() + 
  labs(x = "Crossref citation count", y = "Number of publications", caption = dcap)

Most cited papers

The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.

DT::datatable(
  papers %>% 
    dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>", 
                               url,"</a>")) %>% 
    dplyr::arrange(desc(citation_count)) %>% 
    dplyr::select(title, url, published.date, citation_count),
  escape = FALSE,
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

Citation count vs time since publication

plotly::ggplotly(
  ggplot(papers, aes(x = published.date, y = citation_count, label = title)) + 
    geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() + 
    geom_smooth() + 
    labs(x = "Date of publication", y = "Crossref citation count", caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Power law of citation count within each half year

Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.

ggplot(papers %>% dplyr::group_by(halfyear) %>% 
         dplyr::arrange(desc(citation_count)) %>%
         dplyr::mutate(idx = seq_along(citation_count)), 
       aes(x = idx, y = citation_count)) + 
  geom_point(alpha = 0.5) + 
  facet_wrap(~ halfyear, scales = "free") + 
  theme_bw() + 
  labs(x = "Index", y = "Crossref citation count", caption = dcap)

Pre-review/review time over time

In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.

ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  labs(x = "Date of pre-review opening", y = "Number of days in pre-review", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) + 
  geom_point() + geom_smooth() + theme_bw() + 
  labs(x = "Date of review opening", y = "Number of days in review", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Languages

Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.

## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
  data.frame(language = l,
             nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))

## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>% 
  setNames(c("language", "bytes")) %>%
  dplyr::mutate(bytes = as.numeric(bytes)) %>%
  dplyr::filter(!is.na(language)) %>%
  dplyr::group_by(language) %>%
  dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
                   nbr_repos_GitHub = length(bytes)) %>%
  dplyr::arrange(desc(nbr_bytes_GitHub))

langs <- dplyr::full_join(langs, langbytes, by = "language")
ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
         dplyr::filter(nbr_submissions_Whedon > 10) %>%
         dplyr::mutate(language = factor(language, levels = language)),
       aes(x = language, y = nbr_submissions_Whedon)) + 
  geom_bar(stat = "identity") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  labs(x = "", y = "Number of submissions", caption = dcap) + 
  theme(axis.title = element_text(size = 15))

DT::datatable(
  langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
  escape = FALSE,
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)
ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) + 
  geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() + 
  theme_bw() + 
  labs(x = "Number of repos using the language",
       y = "Total number of bytes of code\nwritten in the language", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Association between number of citations and number of stars of the GitHub repo

ggplotly(
  ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
                     label = title)) + 
    geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() + 
    theme_bw() + 
    labs(x = "Crossref citation count", y = "Number of stars, GitHub repo", 
         caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Distribution of time between GitHub repo creation and JOSS submission

ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from repo creation to JOSS pre-review start", 
       caption = dcap) + 
  theme(axis.title = element_text(size = 15))

Distribution of time between JOSS acceptance and last commit

ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
  geom_histogram(bins = 50) + 
  theme_bw() + 
  labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
       caption = dcap) + 
  theme(axis.title = element_text(size = 15)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

Number of authors per paper

List the papers with the largest number of authors, and display the distribution of the number of authors per paper, for papers with at most 20 authors.

## Papers with largest number of authors
papers %>% dplyr::arrange(desc(nbr_authors)) %>% 
  dplyr::select(title, published.date, url, nbr_authors) %>%
  as.data.frame() %>% head(10)
##                                                                                                                          title
## 1                                                                                    SunPy: A Python package for Solar Physics
## 2                                                        ENZO: An Adaptive Mesh Refinement Code for Astrophysics (Version 2.6)
## 3  The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained
## 4                                                     GRChombo: An adaptable numerical relativity code for fundamental physics
## 5                                                                                       PyBIDS: Python tools for BIDS datasets
## 6                                       DataLad: distributed system for joint management of code, data, and their relationship
## 7                                                                            Chaste: Cancer, Heart and Soft Tissue Environment
## 8                                                                           spam: Software for Practical Analysis of Materials
## 9                                                       SNEWPY: A Data Pipeline from Supernova Simulations to Neutrino Signals
## 10                                                                                       VIVO: a system for research discovery
##    published.date                                   url nbr_authors
## 1      2020-02-14 http://dx.doi.org/10.21105/joss.01832         124
## 2      2019-10-03 http://dx.doi.org/10.21105/joss.01636          55
## 3      2021-02-21 http://dx.doi.org/10.21105/joss.02807          38
## 4      2021-12-10 http://dx.doi.org/10.21105/joss.03703          32
## 5      2019-08-12 http://dx.doi.org/10.21105/joss.01294          31
## 6      2021-07-01 http://dx.doi.org/10.21105/joss.03262          31
## 7      2020-03-13 http://dx.doi.org/10.21105/joss.01848          29
## 8      2020-07-13 http://dx.doi.org/10.21105/joss.02286          27
## 9      2021-11-27 http://dx.doi.org/10.21105/joss.03772          26
## 10     2019-07-26 http://dx.doi.org/10.21105/joss.01182          25
nbins <- max(papers$nbr_authors[papers$nbr_authors <= 20])
ggplot(papers %>% dplyr::filter(nbr_authors <= 20),
  aes(x = nbr_authors)) + 
  geom_histogram(bins = nbins, fill = "lightgrey", color = "grey50") + 
  theme_bw() + 
  facet_wrap(~ year(published.date), scales = "free_y") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Number of authors",
       y = "Number of publications with\na given number of authors", 
       caption = dcap)

ggplot(papers %>% 
         dplyr::mutate(nbr_authors = replace(nbr_authors, nbr_authors > 5, ">5")) %>%
         dplyr::mutate(nbr_authors = factor(nbr_authors, levels = c("1", "2", "3", 
                                                                    "4", "5", ">5"))) %>%
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(year = factor(year)) %>%
         dplyr::group_by(year, nbr_authors, .drop = FALSE) %>%
         dplyr::summarize(n = n()) %>%
         dplyr::mutate(freq = n/sum(n)) %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = freq, fill = nbr_authors)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Number of\nauthors", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Fraction of submissions", caption = dcap)

Number of authors vs number of contributors to the GitHub repo

Note that points are slightly jittered to reduce the overlap.

plotly::ggplotly(
  ggplot(papers, aes(x = nbr_authors, y = repo_nbr_contribs_2ormore, label = title)) + 
    geom_abline(slope = 1, intercept = 0) + 
    geom_jitter(width = 0.05, height = 0.05, alpha = 0.5) + 
    # geom_point(alpha = 0.5) + 
    theme_bw() + 
    scale_x_sqrt() + scale_y_sqrt() + 
    labs(x = "Number of authors", 
         y = "Number of contributors\nwith at least 2 commits", 
         caption = dcap) + 
    theme(axis.title = element_text(size = 15)),
  tooltip = c("label", "x", "y")
)

Number of reviewers per paper

Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.

ggplot(papers %>%
         dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
         dplyr::mutate(year = year(published.date)),
       aes(x = nbr_reviewers)) + geom_bar() + 
  facet_wrap(~ year) + theme_bw() + 
  labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)

Most active reviewers

Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.

reviewers <- papers %>% 
  dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
  dplyr::mutate(year = year(published.date)) %>%
  dplyr::select(reviewers, year) %>%
  tidyr::separate_rows(reviewers, sep = ",")

## Most active reviewers
DT::datatable(
  reviewers %>% dplyr::group_by(reviewers) %>%
    dplyr::summarize(nbr_reviews = length(year),
                     timespan = paste(unique(c(min(year), max(year))), 
                                      collapse = " - ")) %>%
    dplyr::arrange(desc(nbr_reviews)),
  escape = FALSE, rownames = FALSE, 
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE)
)

Number of papers per editor and year

ggplot(papers %>% 
         dplyr::mutate(year = year(published.date),
                       `r/pyOpenSci` = factor(
                         grepl("rOpenSci|pyOpenSci", prerev_labels),
                         levels = c("TRUE", "FALSE"))), 
       aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) + 
  theme_bw() + facet_wrap(~ year, ncol = 1) + 
  scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  labs(x = "Editor", y = "Number of submissions", caption = dcap)

Distribution of software repo licenses

all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
                   grep("bsd", all_licenses, value = TRUE),
                   grep("mit", all_licenses, value = TRUE),
                   grep("gpl", all_licenses, value = TRUE),
                   grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>% 
         dplyr::mutate(repo_license = factor(repo_license, 
                                             levels = license_levels)),
       aes(x = repo_license)) +
  geom_bar() + 
  theme_bw() + 
  labs(x = "Software license", y = "Number of submissions", caption = dcap) + 
  theme(axis.title = element_text(size = 15),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  facet_wrap(~ year(published.date), scales = "free_y")

## For plots below, replace licenses present in less 
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])
ggplot(papers %>% 
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(repo_license = replace(repo_license, 
                                              repo_license %in% to_replace,
                                              "other")) %>%
         dplyr::mutate(year = factor(year), 
                       repo_license = factor(
                         repo_license, 
                         levels = license_levels[license_levels %in% repo_license]
                       )) %>%
         dplyr::group_by(year, repo_license, .drop = FALSE) %>%
         dplyr::count() %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = n, fill = repo_license)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Software\nlicense", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Number of submissions", caption = dcap)

ggplot(papers %>% 
         dplyr::mutate(year = year(published.date)) %>%
         dplyr::mutate(repo_license = replace(repo_license, 
                                              repo_license %in% to_replace,
                                              "other")) %>%
         dplyr::mutate(year = factor(year), 
                       repo_license = factor(
                         repo_license, 
                         levels = license_levels[license_levels %in% repo_license]
                       )) %>%
         dplyr::group_by(year, repo_license, .drop = FALSE) %>%
         dplyr::summarize(n = n()) %>%
         dplyr::mutate(freq = n/sum(n)) %>%
         dplyr::mutate(year = as.integer(as.character(year))), 
       aes(x = year, y = freq, fill = repo_license)) + geom_area() + 
  theme_minimal() + 
  scale_fill_brewer(palette = "Set1", name = "Software\nlicense", 
                    na.value = "grey") + 
  theme(axis.title = element_text(size = 15)) + 
  labs(x = "Year", y = "Fraction of submissions", caption = dcap)

Most common GitHub repo topics

a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)

colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
  names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
  random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE, 
  colors = colors, scale = c(10, 0.1), random.color = TRUE,
  ordered.colors = FALSE, vfont = c("serif", "plain")
)

DT::datatable(as.data.frame(topicfreq) %>% 
                dplyr::rename(topic = a, nbr_repos = Freq) %>%
                dplyr::arrange(desc(nbr_repos)),
  escape = FALSE, rownames = FALSE, 
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE))

Citation analysis

Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.

Get citing papers for each submission

citations <- tryCatch({
  citecorp::oc_coci_cites(doi = papers$alternative.id) %>%
    dplyr::distinct() %>%
    dplyr::mutate(citation_info_obtained = as.character(lubridate::today()))
}, error = function(e) {
  NULL
})
dim(citations)
## [1] 12427     8
if (!is.null(citations)) {
  citations <- citations %>% 
    dplyr::filter(!(oci %in% citations_archive$oci))
  
  tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
    dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
                  contains("type"), contains("publisher"), contains("prefix"))
  citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
  
  ## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume 
  ## that they can be 
  ## identified from the prefix 10.1101 - set the container.title 
  ## for these records manually; we may or may not want to count these
  ## (would it count citations twice, both preprint and publication?)
  citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
  
  ## JOSS is represented by 'The Journal of Open Source Software' as well as 
  ## 'Journal of Open Source Software'
  citations$container.title[citations$container.title == 
                              "Journal of Open Source Software"] <- 
    "The Journal of Open Source Software"
  
  ## Remove real self citations (cited DOI = citing DOI)
  citations <- citations %>% dplyr::filter(cited != citing)
  
  ## Merge with the archive
  citations <- dplyr::bind_rows(citations, citations_archive)
} else {
  citations <- citations_archive
  if (is.null(citations[["citation_info_obtained"]])) {
    citations$citation_info_obtained <- NA_character_
  }
}

citations$citation_info_obtained[is.na(citations$citation_info_obtained)] <- 
  "2021-08-11"

write.table(citations, file = "joss_submission_citations.tsv",
            row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)

Summary statistics

## Latest successful update of new citation data
max(as.Date(citations$citation_info_obtained))
## [1] "2021-12-01"
## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))
## [1] 851
## Number of JOSS papers with >0 citations according to Crossref
length(which(papers$citation_count > 0))
## [1] 953
## Number of citations from Open Citations Corpus vs Crossref
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
  dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
                     dplyr::tally() %>%
                     dplyr::mutate(n = replace(n, is.na(n), 0)),
                   by = c("doi" = "cited"))
## Total citation count Crossref
sum(df0$citation_count, na.rm = TRUE)
## [1] 15403
## Total citation count Open Citations Corpus
sum(df0$n, na.rm = TRUE)
## [1] 12408
## Ratio of total citation count Open Citations Corpus/Crossref
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)
## [1] 0.8055574
ggplot(df0, aes(x = citation_count, y = n)) + 
  geom_abline(slope = 1, intercept = 0) + 
  geom_point(size = 3, alpha = 0.5) + 
  labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
       caption = dcap) + 
  theme_bw()

## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) + 
  geom_abline(slope = 1, intercept = 0) + 
  geom_point(size = 3, alpha = 0.5) + 
  labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
       caption = dcap) + 
  theme_bw() + 
  coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))

## Number of journals citing JOSS papers
length(unique(citations$container.title))
## [1] 3166
length(unique(citations$issn))
## [1] 2762

Most citing journals

topcit <- citations %>% dplyr::group_by(container.title) %>%
  dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
                   nbr_cited_joss_papers = length(unique(cited)),
                   nbr_citing_papers = length(unique(citing)),
                   nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
                   fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
                     nbr_citations_of_joss_papers, digits = 3)) %>%
  dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
  escape = FALSE, rownames = FALSE, 
  filter = list(position = 'top', clear = FALSE),
  options = list(scrollX = TRUE))
plotly::ggplotly(
  ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
                     label = container.title)) + 
    geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") + 
    geom_point(size = 3, alpha = 0.5) + 
    theme_bw() + 
    labs(caption = dcap, x = "Number of citations of JOSS papers",
         y = "Number of cited JOSS papers")
)
plotly::ggplotly(
  ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
                     label = container.title)) + 
    geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") + 
    geom_point(size = 3, alpha = 0.5) + 
    theme_bw() + 
    coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) + 
    labs(caption = dcap, x = "Number of citations of JOSS papers",
         y = "Number of cited JOSS papers")
)
write.table(topcit, file = "joss_submission_citations_byjournal.tsv",
            row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)

Save object

The tibble object with all data collected above is serialized to a file that can be downloaded and reused.

head(papers) %>% as.data.frame()
##        alternative.id                     container.title    created  deposited
## 1 10.21105/joss.00808     Journal of Open Source Software 2019-03-18 2019-11-17
## 2 10.21105/joss.00050 The Journal of Open Source Software 2016-12-07 2017-10-24
## 3 10.21105/joss.03313     Journal of Open Source Software 2021-08-31 2021-08-31
## 4 10.21105/joss.01978     Journal of Open Source Software 2020-04-21 2020-04-21
## 5 10.21105/joss.02583     Journal of Open Source Software 2020-09-26 2020-09-26
## 6 10.21105/joss.02328     Journal of Open Source Software 2020-07-09 2020-07-09
##   published.print                 doi    indexed      issn issue     issued
## 1      2019-03-18 10.21105/joss.00808 2021-05-09 2475-9066    35 2019-03-18
## 2      2016-12-06 10.21105/joss.00050 2021-12-09 2475-9066     8 2016-12-06
## 3      2021-08-31 10.21105/joss.03313 2021-12-08 2475-9066    64 2021-08-31
## 4      2020-04-21 10.21105/joss.01978 2021-12-08 2475-9066    48 2020-04-21
## 5      2020-09-26 10.21105/joss.02583 2021-12-08 2475-9066    53 2020-09-26
## 6      2020-07-09 10.21105/joss.02328 2021-12-09 2475-9066    51 2020-07-09
##   member page   prefix        publisher score   source reference.count
## 1   8722  808 10.21105 The Open Journal     0 Crossref               1
## 2   8722   50 10.21105 The Open Journal     0 Crossref               2
## 3   8722 3313 10.21105 The Open Journal     0 Crossref              18
## 4   8722 1978 10.21105 The Open Journal     0 Crossref               5
## 5   8722 2583 10.21105 The Open Journal     0 Crossref              28
## 6   8722 2328 10.21105 The Open Journal     0 Crossref               5
##   references.count is.referenced.by.count
## 1                1                      0
## 2                2                      2
## 3               18                      0
## 4                5                      1
## 5               28                      0
## 6                5                      3
##                                                                                               title
## 1 GMSimViz: Automated 3D Visualization of Ground Motion Simulation with Generic Mapping Tools (GMT)
## 2                                                                     MassMine: Your Access To Data
## 3                    diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation
## 4         Museo ToolBox: A Python library for remote sensing including a new way to handle rasters.
## 5           emba: R package for analysis and visualization of biomarkers in boolean model ensembles
## 6                                                  CollatriX: A GUI to collate MorphoMetriX outputs
##              type                                   url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.00808      4
## 2 journal-article http://dx.doi.org/10.21105/joss.00050      1
## 3 journal-article http://dx.doi.org/10.21105/joss.03313      6
## 4 journal-article http://dx.doi.org/10.21105/joss.01978      5
## 5 journal-article http://dx.doi.org/10.21105/joss.02583      5
## 6 journal-article http://dx.doi.org/10.21105/joss.02328      5
##   short.container.title
## 1                  JOSS
## 2                  JOSS
## 3                  JOSS
## 4                  JOSS
## 5                  JOSS
## 6                  JOSS
##                                                                                                                                                                                                                                                                                                                                                                                                                                           author
## 1 http://orcid.org/0000-0001-5442-2960, http://orcid.org/0000-0001-7761-077X, http://orcid.org/0000-0001-7670-8214, http://orcid.org/0000-0003-0472-7939, http://orcid.org/0000-0002-4450-314X, http://orcid.org/0000-0002-7717-280X, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, Viktor, Yuezhang, Sung, Jason, Brendon, Hoby, Polak, Zhu, Bae, Motha, Bradley, Razafindrakoto, first, additional, additional, additional, additional, additional
## 2                                                                                                                                                                                                                                                                                            http://orcid.org/0000-0002-0695-7765, http://orcid.org/0000-0002-5156-3044, FALSE, FALSE, Nicholas, Aaron, M Van Horn, Beveridge, first, additional
## 3                                                                                                                                       http://orcid.org/0000-0003-2990-2190, http://orcid.org/0000-0003-2007-929X, http://orcid.org/0000-0002-4370-9971, http://orcid.org/0000-0002-3406-6214, FALSE, FALSE, FALSE, FALSE, Amanda, Benjamin, Travis, Chris, Smith, St&amp;uuml;rmer, Thurber, Vernon, first, additional, additional, additional
## 4                                                                                                                                                                                                                                                                                                                                                                          http://orcid.org/0000-0002-1558-0816, FALSE, Nicolas, Karasiak, first
## 5                                                                                                                                                                                                                            http://orcid.org/0000-0002-3609-8674, http://orcid.org/0000-0002-1171-9876, http://orcid.org/0000-0002-3357-425X, FALSE, FALSE, FALSE, John, Martin, Åsmund, Zobolas, Kuiper, Flobak, first, additional, additional
## 6                                                                                                                                                                                                                                                                                                                                              http://orcid.org/0000-0001-7763-7761, NA, FALSE, NA, Clara, Kc, Bird, Bierlich, first, additional
##   citation_count
## 1              0
## 2              2
## 3              0
## 4              1
## 5              0
## 6              3
##                                                                                           api_title
## 1 GMSimViz: Automated 3D Visualization of Ground Motion Simulation with Generic Mapping Tools (GMT)
## 2                                                                     MassMine: Your Access To Data
## 3                    diyepw: A Python package for Do-It-Yourself EnergyPlus weather file generation
## 4         Museo ToolBox: A Python library for remote sensing including a new way to handle rasters.
## 5           emba: R package for analysis and visualization of biomarkers in boolean model ensembles
## 6                                                  CollatriX: A GUI to collate MorphoMetriX outputs
##   api_state          editor                      reviewers nbr_reviewers
## 1  accepted         @lheagy          @leouieda,@hugoledoux             2
## 2  accepted        @mgymrek                 @julianmcauley             1
## 3  accepted    @timtroendle @samuelduchesne,@ritwikagarwal             2
## 4  accepted      @kbarnhart        @cmillion,@mollenburger             2
## 5  accepted         @mikldk        @sahilseth,@edifice1989             2
## 6  accepted @KristinaRiemer        @evaristor,@katmratliff             2
##                                    repo_url review_issue_id prereview_issue_id
## 1       https://github.com/ucgmsim/GMSimViz             808                778
## 2          https://github.com/n3mo/massmine              50                 NA
## 3       https://github.com/IMMM-SFA/diyepw/            3313               3187
## 4 https://github.com/nkarasiak/MuseoToolBox            1978               1950
## 5          https://github.com/bblodfon/emba            2583               2534
## 6  https://github.com/cbirdferrer/collatrix            2328               2247
##                   languages                             archive_doi
## 1                Python,TeX  https://doi.org/10.5281/zenodo.2590852
## 2          Shell,Scheme,TeX http://dx.doi.org/10.5281/zenodo.193078
## 3                Python,TeX  https://doi.org/10.5281/zenodo.5258122
## 4 Makefile,Shell,Python,TeX  https://doi.org/10.5281/zenodo.3759215
## 5                     R,TeX  https://doi.org/10.5281/zenodo.4043085
## 6                Python,TeX  https://doi.org/10.5281/zenodo.3937611
##                                                                                 review_title
## 1                                                                                   GMSimViz
## 2                                                              MassMine: Your Access To Data
## 3           diyepw: A Python package for Do-It-Yourself EnergyPlus 1 weather file generation
## 4 Museo ToolBox : a python library for remote sensing including a new way to handle rasters.
## 5    emba: R package for analysis and visualization of biomarkers in boolean model ensembles
## 6                                           CollatriX: A GUI to collate MorphoMetriX outputs
##   review_number review_state review_opened review_closed review_ncomments
## 1           808       closed    2018-07-04    2019-03-18               95
## 2            50       closed    2016-08-16    2016-12-07               42
## 3          3313       closed    2021-05-26    2021-08-31               68
## 4          1978       closed    2019-12-20    2020-04-21               85
## 5          2583       closed    2020-08-19    2020-09-26               76
## 6          2328       closed    2020-06-12    2020-07-09               42
##                                review_labels
## 1        accepted,recommend-accept,published
## 2        accepted,recommend-accept,published
## 3 accepted,Python,recommend-accept,published
## 4        accepted,recommend-accept,published
## 5  accepted,TeX,R,recommend-accept,published
## 6        accepted,recommend-accept,published
##                                                                                 prerev_title
## 1                                                                                   GMSimViz
## 2                                                                                       <NA>
## 3           diyepw: A Python package for Do-It-Yourself EnergyPlus 1 weather file generation
## 4 Museo ToolBox : a python library for remote sensing including a new way to handle rasters.
## 5    emba: R package for analysis and visualization of biomarkers in boolean model ensembles
## 6                                           CollatriX: A GUI to collate MorphoMetriX outputs
##   prerev_state prerev_opened prerev_closed prerev_ncomments
## 1       closed    2018-06-15    2018-07-04               30
## 2         <NA>          <NA>          <NA>               NA
## 3       closed    2021-04-17    2021-05-26               25
## 4       closed    2019-12-12    2019-12-20               27
## 5       closed    2020-07-28    2020-08-19               32
## 6       closed    2020-05-23    2020-06-12               21
##           prerev_labels days_in_pre days_in_rev to_review repo_created
## 1            TeX,Python     19 days    257 days      TRUE   2018-04-17
## 2                  <NA>     NA days    113 days      TRUE   2014-04-04
## 3                Python     39 days     97 days      TRUE   2020-08-26
## 4 Shell,Python,Makefile      8 days    123 days      TRUE   2018-06-20
## 5                 TeX,R     22 days     38 days      TRUE   2019-06-03
## 6                           20 days     27 days      TRUE   2020-02-26
##   repo_updated repo_pushed repo_nbr_stars repo_language
## 1   2021-09-16  2021-07-26              7        Python
## 2   2021-10-07  2021-10-07             70        Scheme
## 3   2021-11-03  2021-11-10              9        Python
## 4   2021-11-12  2020-08-03             21        Python
## 5   2021-01-07  2021-01-07              0             R
## 6   2021-11-04  2021-11-04              0        Python
##                             repo_languages_bytes
## 1                         Python:265312,TeX:1244
## 2               Scheme:111163,Shell:2631,TeX:478
## 3                                  Python:110591
## 4 Python:222335,TeX:2524,Makefile:1556,Shell:854
## 5                             R:211165,TeX:45603
## 6                          Python:62562,TeX:1826
##                                                                                       repo_topics
## 1                                                                                                
## 2                                                                                                
## 3                                                                                                
## 4                                                                                                
## 5                                                          r,r-package,biomarkers,ensemble-models
## 6 length-measurement,altitude-calibration,body-volume,whales,photogrammetry,gui-application,pyqt5
##   repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1          mit                 9                         8         2021-11-29
## 2      gpl-3.0                 1                         0         2021-12-15
## 3 bsd-2-clause                 4                         4         2021-12-09
## 4      gpl-3.0                 7                         6         2021-11-29
## 5        other                 2                         1         2021-12-15
## 6          mit                 1                         1         2021-12-15
##   published.date halfyear nbr_authors
## 1     2019-03-18   2019H1           6
## 2     2016-12-06   2016H2           2
## 3     2021-08-31   2021H2           4
## 4     2020-04-21   2020H1           1
## 5     2020-09-26   2020H2           3
## 6     2020-07-09   2020H2           2
saveRDS(papers, file = "joss_submission_analytics.rds")

To read the current version of this file directly from GitHub, use the following code:

papers <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true")))

Session info

sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] readr_2.1.1        citecorp_0.3.0     plotly_4.10.0      DT_0.20           
##  [5] jsonlite_1.7.2     purrr_0.3.4        gh_1.3.0           lubridate_1.8.0   
##  [9] ggplot2_3.3.5      tidyr_1.1.4        dplyr_1.0.7        rcrossref_1.1.0.99
## [13] tibble_3.1.6      
## 
## loaded via a namespace (and not attached):
##  [1] viridis_0.6.2      httr_1.4.2         sass_0.4.0         splines_4.1.2     
##  [5] bit64_4.0.5        vroom_1.5.7        viridisLite_0.4.0  bslib_0.3.1       
##  [9] shiny_1.7.1        highr_0.9          triebeard_0.3.0    urltools_1.7.3    
## [13] yaml_2.2.1         lattice_0.20-45    pillar_1.6.4       glue_1.5.1        
## [17] digest_0.6.29      RColorBrewer_1.1-2 promises_1.2.0.1   colorspace_2.0-2  
## [21] Matrix_1.3-4       htmltools_0.5.2    httpuv_1.6.4       plyr_1.8.6        
## [25] pkgconfig_2.0.3    httpcode_0.3.0     xtable_1.8-4       gitcreds_0.1.1    
## [29] scales_1.1.1       whisker_0.4        later_1.3.0        tzdb_0.2.0        
## [33] mgcv_1.8-38        generics_0.1.1     farver_2.1.0       ellipsis_0.3.2    
## [37] withr_2.4.3        lazyeval_0.2.2     cli_3.1.0          magrittr_2.0.1    
## [41] crayon_1.4.2       mime_0.12          evaluate_0.14      fansi_0.5.0       
## [45] nlme_3.1-153       xml2_1.3.3         tools_4.1.2        data.table_1.14.2 
## [49] hms_1.1.1          lifecycle_1.0.1    stringr_1.4.0      munsell_0.5.0     
## [53] compiler_4.1.2     jquerylib_0.1.4    rlang_0.4.12       grid_4.1.2        
## [57] htmlwidgets_1.5.4  crosstalk_1.2.0    miniUI_0.1.1.1     labeling_0.4.2    
## [61] rmarkdown_2.11     gtable_0.3.0       curl_4.3.2         fauxpas_0.5.0     
## [65] R6_2.5.1           gridExtra_2.3      knitr_1.36         fastmap_1.1.0     
## [69] bit_4.0.4          utf8_1.2.2         stringi_1.7.6      parallel_4.1.2    
## [73] crul_1.2.0         Rcpp_1.0.7         vctrs_0.3.8        wordcloud_2.6     
## [77] tidyselect_1.1.1   xfun_0.29